Search

In this work, a total of nine ultra-high performance concrete (UHPC) filled steel tubular columns (UHPCFSTs) were fabricated and tested under cyclic loading. The results indicated that UHPCFSTs mainly exhibited flexural failure mode, compression-flexure failure mode and welding line fracture, and it changed from flexural failure mode to compression-flexure failure mode when the axial compression ratios exceeded 0.3. The elastic and ultimate drift ratios of UHPCFSTs were within the ranges of 1.37–2.05% and 2.38–4.32%, respectively, and the equivalent viscous damping coefficients were within the range of 0.197–0.358 when they failed, UHPCFSTs could sever as good choices in high seismic intensity areas. [ABSTRACT FROM AUTHOR]

The design, analysis, and measurement of a novel frequency selective surface (FSS) are proposed, which works with high selectivity in X-band and wide out of band rejection in C-band and Ku-band. The proposed FSS is achieved by cascading two-layer periodic arrays of unit cells based on square loop structure. Multiple transmission zeros are obtained for broadening the stopband on the both sides of the passband. The presented double-layer FSS is insensitive to the variation of incident angle and polarization of a striking plane electromagnetic wave. By studying the electric fields and currents excited in the proposed structure, it explains the passband characteristics and the multiple transmission zeros in the two stopbands. Experimental verifications are carried out, which are consistent with the simulated results. [ABSTRACT FROM PUBLISHER]

Abstract Running is a highly popular form of exercise, while incorrect running posture over an extended period can lead to severe knee injuries. Smart textiles have recently demonstrated significant potential for continuous motion monitoring. This study involved the design and development of a smart legging with a resistive textile sensor network to monitor lower body motion. The study consists of three main parts. Firstly, we tested textile sensors in terms of linearity and robustness to determine the basic sensor unit that can monitor the characteristics of running postures. Next, optimal sensor placement was determined through comparison experiments, and a sensor network was proposed. Finally, based on the LSTM model with data gathered from 6 participants, we developed the smart legging system that is capable of identifying three types of improper running postures and normal postures with 99.1% accuracy. The evaluation revealed that the smart legging system had the potential to help users adjust their running postures to prevent knee injury through continuous monitoring and multi-modal feedback. [ABSTRACT FROM AUTHOR]